This is a starting point for making sense of the tools used for analyzing Mozilla data. There are different tools available, each with their own strengths, tailored to a variety of use cases and skill sets.
- High-level tools
- Lower-level tools
These web-based tools do not require specialized technical knowledge (e.g. how to write an SQL query, deep knowledge of BigQuery). This is where you should start.
The Mozilla Growth & Usage Dashboard (GUD) is a tool to visualize growth metrics in a standard way across Mozilla’s products. This is the first place you should look if you have a question like "how many people are using X?".
The Glean Aggregated Metrics Dashboard (GLAM) is an interactive dashboard that is Mozilla’s primary self-service tool for examining the distributions of values of specific individual telemetry metrics, over time and across different user populations. It is similar to GUD in that it is meant to be usable by everyone; no specific data analysis or coding skills are needed. But while GUD is focused on a relatively small number of high level, derived product metrics about user engagement (e.g. MAU, DAU, retention, etc) GLAM is focused on a diverse and plentiful set of probes and data points that engineers capture in code and transmit back from Firefox and other Mozilla products.
As of this writing, GLAM is in active development but is already usable.
The Telemetry Measurement Dashboard (TMO) site is the 'venerable standby' of Firefox telemetry analysis tools. It is the predecessor to GLAM (see above) and is still maintained until GLAM is considered production ready.
These tools require more specialized knowledge to use.
sql.telemetry.mozilla.org (STMO) site
is an instance of the very fine Redash software, allowing
for SQL-based exploratory analysis and visualization / dashboard
construction. Requires (surprise!) familiarity with SQL, and for your data to
be explicitly exposed as an STMO data source. You can learn more about how to use it in Introduction to STMO. Bugs or feature requests can be reported in Mozilla's issue tracker.
⚠ Databricks will be available until the end of 2020.
Databricks offers a notebook interface with shared, always-on, autoscaling cluster (attaching your notebooks to
shared_serverless_python3 is the best way to start).
Convenient for quick data investigations. Users are advised to join the